Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/2461
Citations
Scopus Web of ScienceĀ® Altmetric
?
?
Type: Journal article
Title: Support vector learning with quadratic programming and adaptive step size barrier-projection
Author: To, K.
Lim, C.
Teo, K.
Liebelt, M.
Citation: Nonlinear Analysis Theory Methods and Applications, 2001; 47(8 Part 8 Special Issue SI):5623-5633
Publisher: Pergamon-Elsevier Science Ltd
Issue Date: 2001
ISSN: 0362-546X
Statement of
Responsibility: 
K. N. To, C. C. Lim, K. L. Teo and M. J. Liebelt
Abstract: We consider a support vector machine training problem involving a quadratic objective function with a single linear equality constraint and a box constraint. Using quadratic surjective space transformation to create a barrier for the gradient method, an iterative support vector learning algorithm is derived. We further derive a stable steepest descent method to find the stop-size in order to reduce the number of iterations to reach the optimal solution. This method offers speed improvement over the fixed step-size gradient method, in particular for QP problems with ill-conditioned Hessian.
Keywords: Support vector machines
quadratic programming
barrier projection method
DOI: 10.1016/S0362-546X(01)00664-2
Description (link): http://www.elsevier.com/wps/find/journaldescription.cws_home/239/description#description
Appears in Collections:Aurora harvest 2
Electrical and Electronic Engineering publications
Environment Institute publications

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.